Marginalized zero-inflated negative binomial regression with application to dental caries.
Marginalized zero-inflated negative binomial regression with application to dental caries.
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DOI：
10.1002/sim.6804
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发表时间：
2016-05-10
影响因子：
2
通讯作者：
Divaris K
中科院分区：
文献类型：
--
作者：
Preisser JS;Das K;Long DL;Divaris K
零膨胀负二项回归模型(ZINB)经常被用于牙科、医疗保健利用、高速公路安全和医学等不同领域，以检查感兴趣的暴露和过度分散的计数结果之间的关系，结果显示许多零。ZINB的回归系数对处于研究中疾病/状况风险的易感亚群和只提供零计数的非易感亚群有潜在的类别解释，其中计数是由负二项分布产生的。然而，ZINB参数不太适合估计总体暴露影响，特别是在量化总体混合人群中的解释变量的影响方面。本文提出了一种独立反应的边际零膨胀负二项回归(MZINB)模型，用于直接对总体边际均值计数进行建模，基于最大似然估计为总体暴露效应提供直接推论。通过仿真研究，将MZINB的有限样本性能与边际零膨胀泊松、泊松和负二项回归进行了比较。MZINB模型被应用于以学校为基础的氟化含氟咀嚼计划的评估，该计划在677名儿童中进行。
The zero-inflated negative binomial regression model (ZINB) is often employed in diverse fields such as dentistry, health care utilization, highway safety, and medicine to examine relationships between exposures of interest and overdispersed count outcomes exhibiting many zeros. The regression coefficients of ZINB have latent class interpretations for a susceptible subpopulation at risk for the disease/condition under study with counts generated from a negative binomial distribution and for a non-susceptible subpopulation that provides only zero counts. The ZINB parameters, however, are not well-suited for estimating overall exposure effects, specifically, in quantifying the effect of an explanatory variable in the overall mixture population. In this paper, a marginalized zero-inflated negative binomial regression (MZINB) model for independent responses is proposed to model the population marginal mean count directly, providing straightforward inference for overall exposure effects based on maximum likelihood estimation. Through simulation studies, the finite sample performance of MZINB is compared to marginalized zero-inflated Poisson, Poisson, and negative binomial regression. The MZINB model is applied in the evaluation of a school-based fluoride mouthrinse program on dental caries in 677 children.